Research Article
Deep Reinforcement Learning Based QoS-Aware Routing in Knowledge-Defined Networking
@INPROCEEDINGS{10.1007/978-3-030-14413-5_2, author={Tran Pham and Yassine Hadjadj-Aoul and Abdelkader Outtagarts}, title={Deep Reinforcement Learning Based QoS-Aware Routing in Knowledge-Defined Networking}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 14th EAI International Conference, Qshine 2018, Ho Chi Minh City, Vietnam, December 3--4, 2018, Proceedings}, proceedings_a={QSHINE}, year={2019}, month={3}, keywords={Knowledge-Defined networking Software-Defined networking Routing Deep Reinforcement Learning}, doi={10.1007/978-3-030-14413-5_2} }- Tran Pham
Yassine Hadjadj-Aoul
Abdelkader Outtagarts
Year: 2019
Deep Reinforcement Learning Based QoS-Aware Routing in Knowledge-Defined Networking
QSHINE
Springer
DOI: 10.1007/978-3-030-14413-5_2
Abstract
Knowledge-Defined networking (KDN) is a concept that relies on Software-Defined networking (SDN) and Machine Learning (ML) in order to operate and optimize data networks. Thanks to SDN, a centralized path calculation can be deployed, thus enhancing the network utilization as well as Quality of Services (QoS). QoS-aware routing problem is a high complexity problem, especially when there are multiple flows coexisting in the same network. Deep Reinforcement Learning (DRL) is an emerging technique that is able to cope with such complex problem. Recent studies confirm the ability of DRL in solving complex routing problems; however, its performance in the network with QoS-sensitive flows has not been addressed. In this paper, we exploit a DRL agent with convolutional neural networks in the context of KDN in order to enhance the performance of QoS-aware routing. The obtained results demonstrate that the proposed approach is able to improve the performance of routing configurations significantly even in complex networks.
